arg_utils.py 71.5 KB
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# SPDX-License-Identifier: Apache-2.0

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# yapf: disable
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import argparse
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import dataclasses
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import json
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import re
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import threading
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import warnings
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from dataclasses import MISSING, dataclass, fields, is_dataclass
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from itertools import permutations
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from typing import (Annotated, Any, Callable, Dict, List, Literal, Optional,
                    Type, TypeVar, Union, cast, get_args, get_origin)
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import torch
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from typing_extensions import TypeIs, deprecated
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import vllm.envs as envs
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from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
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                         ConfigFormat, ConfigType, DecodingConfig,
                         DetailedTraceModules, Device, DeviceConfig,
                         DistributedExecutorBackend, GuidedDecodingBackend,
                         GuidedDecodingBackendV1, HfOverrides, KVEventsConfig,
                         KVTransferConfig, LoadConfig, LoadFormat, LoRAConfig,
                         ModelConfig, ModelDType, ModelImpl, MultiModalConfig,
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                         ObservabilityConfig, ParallelConfig, PoolerConfig,
                         PrefixCachingHashAlgo, PromptAdapterConfig,
                         SchedulerConfig, SchedulerPolicy, SpeculativeConfig,
                         TaskOption, TokenizerMode, TokenizerPoolConfig,
                         VllmConfig, get_attr_docs, get_field)
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from vllm.executor.executor_base import ExecutorBase
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization import QuantizationMethods
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from vllm.plugins import load_general_plugins
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from vllm.reasoning import ReasoningParserManager
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from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
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from vllm.transformers_utils.utils import check_gguf_file
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from vllm.usage.usage_lib import UsageContext
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from vllm.utils import (FlexibleArgumentParser, GiB_bytes, is_in_doc_build,
                        is_in_ray_actor)
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# yapf: enable
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logger = init_logger(__name__)

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# object is used to allow for special typing forms
T = TypeVar("T")
TypeHint = Union[type[Any], object]
TypeHintT = Union[type[T], object]

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def parse_type(return_type: Callable[[str], T]) -> Callable[[str], T]:
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    def _parse_type(val: str) -> T:
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        try:
            if return_type is json.loads and not re.match("^{.*}$", val):
                return cast(T, nullable_kvs(val))
            return return_type(val)
        except ValueError as e:
            raise argparse.ArgumentTypeError(
                f"Value {val} cannot be converted to {return_type}.") from e
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    return _parse_type


def optional_type(
        return_type: Callable[[str], T]) -> Callable[[str], Optional[T]]:

    def _optional_type(val: str) -> Optional[T]:
        if val == "" or val == "None":
            return None
        return parse_type(return_type)(val)

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    return _optional_type
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def union_dict_and_str(val: str) -> Optional[Union[str, dict[str, str]]]:
    if not re.match("^{.*}$", val):
        return str(val)
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    return optional_type(json.loads)(val)
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@deprecated(
    "Passing a JSON argument as a string containing comma separated key=value "
    "pairs is deprecated. This will be removed in v0.10.0. Please use a JSON "
    "string instead.")
def nullable_kvs(val: str) -> dict[str, int]:
    """Parses a string containing comma separate key [str] to value [int]
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    pairs into a dictionary.

    Args:
        val: String value to be parsed.

    Returns:
        Dictionary with parsed values.
    """
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    out_dict: dict[str, int] = {}
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    for item in val.split(","):
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        kv_parts = [part.lower().strip() for part in item.split("=")]
        if len(kv_parts) != 2:
            raise argparse.ArgumentTypeError(
                "Each item should be in the form KEY=VALUE")
        key, value = kv_parts
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        try:
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            parsed_value = int(value)
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        except ValueError as exc:
            msg = f"Failed to parse value of item {key}={value}"
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            raise argparse.ArgumentTypeError(msg) from exc

        if key in out_dict and out_dict[key] != parsed_value:
            raise argparse.ArgumentTypeError(
                f"Conflicting values specified for key: {key}")
        out_dict[key] = parsed_value
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    return out_dict


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def is_type(type_hint: TypeHint, type: TypeHintT) -> TypeIs[TypeHintT]:
    """Check if the type hint is a specific type."""
    return type_hint is type or get_origin(type_hint) is type


def contains_type(type_hints: set[TypeHint], type: TypeHintT) -> bool:
    """Check if the type hints contain a specific type."""
    return any(is_type(type_hint, type) for type_hint in type_hints)


def get_type(type_hints: set[TypeHint], type: TypeHintT) -> TypeHintT:
    """Get the specific type from the type hints."""
    return next((th for th in type_hints if is_type(th, type)), None)


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def literal_to_kwargs(type_hints: set[TypeHint]) -> dict[str, Any]:
    """Convert Literal type hints to argparse kwargs."""
    type_hint = get_type(type_hints, Literal)
    choices = get_args(type_hint)
    choice_type = type(choices[0])
    if not all(isinstance(choice, choice_type) for choice in choices):
        raise ValueError(
            "All choices must be of the same type. "
            f"Got {choices} with types {[type(c) for c in choices]}")
    return {"type": choice_type, "choices": sorted(choices)}


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def is_not_builtin(type_hint: TypeHint) -> bool:
    """Check if the class is not a built-in type."""
    return type_hint.__module__ != "builtins"


def get_kwargs(cls: ConfigType) -> dict[str, Any]:
    cls_docs = get_attr_docs(cls)
    kwargs = {}
    for field in fields(cls):
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        # Get the set of possible types for the field
        type_hints: set[TypeHint] = set()
        if get_origin(field.type) in {Union, Annotated}:
            type_hints.update(get_args(field.type))
        else:
            type_hints.add(field.type)

        # If the field is a dataclass, we can use the model_validate_json
        generator = (th for th in type_hints if is_dataclass(th))
        dataclass_cls = next(generator, None)

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        # Get the default value of the field
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        if field.default is not MISSING:
            default = field.default
        elif field.default_factory is not MISSING:
            if is_dataclass(field.default_factory) and is_in_doc_build():
                default = {}
            else:
                default = field.default_factory()
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        # Get the help text for the field
        name = field.name
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        help = cls_docs[name].strip()
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        # Escape % for argparse
        help = help.replace("%", "%%")

        # Initialise the kwargs dictionary for the field
        kwargs[name] = {"default": default, "help": help}

        # Set other kwargs based on the type hints
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        json_tip = "\n\nShould be a valid JSON string."
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        if dataclass_cls is not None:
            dataclass_init = lambda x, f=dataclass_cls: f(**json.loads(x))
            # Special case for configs with a from_cli method
            if hasattr(dataclass_cls, "from_cli"):
                from_cli = dataclass_cls.from_cli
                dataclass_init = lambda x, f=from_cli: f(x)
            kwargs[name]["type"] = dataclass_init
            kwargs[name]["help"] += json_tip
        elif contains_type(type_hints, bool):
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            # Creates --no-<name> and --<name> flags
            kwargs[name]["action"] = argparse.BooleanOptionalAction
        elif contains_type(type_hints, Literal):
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            kwargs[name].update(literal_to_kwargs(type_hints))
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        elif contains_type(type_hints, tuple):
            type_hint = get_type(type_hints, tuple)
            types = get_args(type_hint)
            tuple_type = types[0]
            assert all(t is tuple_type for t in types if t is not Ellipsis), (
                "All non-Ellipsis tuple elements must be of the same "
                f"type. Got {types}.")
            kwargs[name]["type"] = tuple_type
            kwargs[name]["nargs"] = "+" if Ellipsis in types else len(types)
        elif contains_type(type_hints, list):
            type_hint = get_type(type_hints, list)
            types = get_args(type_hint)
            assert len(types) == 1, (
                "List type must have exactly one type. Got "
                f"{type_hint} with types {types}")
            kwargs[name]["type"] = types[0]
            kwargs[name]["nargs"] = "+"
        elif contains_type(type_hints, int):
            kwargs[name]["type"] = int
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            # Special case for large integers
            if name in {"max_model_len"}:
                kwargs[name]["type"] = human_readable_int
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        elif contains_type(type_hints, float):
            kwargs[name]["type"] = float
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        elif contains_type(type_hints,
                           dict) and (contains_type(type_hints, str) or any(
                               is_not_builtin(th) for th in type_hints)):
            kwargs[name]["type"] = union_dict_and_str
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        elif contains_type(type_hints, dict):
            # Dict arguments will always be optional
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            kwargs[name]["type"] = parse_type(json.loads)
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            kwargs[name]["help"] += json_tip
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        elif (contains_type(type_hints, str)
              or any(is_not_builtin(th) for th in type_hints)):
            kwargs[name]["type"] = str
        else:
            raise ValueError(
                f"Unsupported type {type_hints} for argument {name}.")

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        # If the type hint was a sequence of literals, use the helper function
        # to update the type and choices
        if get_origin(kwargs[name].get("type")) is Literal:
            kwargs[name].update(literal_to_kwargs({kwargs[name]["type"]}))

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        # If None is in type_hints, make the argument optional.
        # But not if it's a bool, argparse will handle this better.
        if type(None) in type_hints and not contains_type(type_hints, bool):
            kwargs[name]["type"] = optional_type(kwargs[name]["type"])
            if kwargs[name].get("choices"):
                kwargs[name]["choices"].append("None")
    return kwargs
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@dataclass
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class EngineArgs:
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    """Arguments for vLLM engine."""
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    model: str = ModelConfig.model
    served_model_name: Optional[Union[
        str, List[str]]] = ModelConfig.served_model_name
    tokenizer: Optional[str] = ModelConfig.tokenizer
    hf_config_path: Optional[str] = ModelConfig.hf_config_path
    task: TaskOption = ModelConfig.task
    skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
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    enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
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    tokenizer_mode: TokenizerMode = ModelConfig.tokenizer_mode
    trust_remote_code: bool = ModelConfig.trust_remote_code
    allowed_local_media_path: str = ModelConfig.allowed_local_media_path
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    download_dir: Optional[str] = LoadConfig.download_dir
    load_format: str = LoadConfig.load_format
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    config_format: str = ModelConfig.config_format
    dtype: ModelDType = ModelConfig.dtype
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    kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
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    seed: Optional[int] = ModelConfig.seed
    max_model_len: Optional[int] = ModelConfig.max_model_len
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    cuda_graph_sizes: list[int] = get_field(SchedulerConfig,
                                            "cuda_graph_sizes")
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    # Note: Specifying a custom executor backend by passing a class
    # is intended for expert use only. The API may change without
    # notice.
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    distributed_executor_backend: Optional[Union[
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        DistributedExecutorBackend,
        Type[ExecutorBase]]] = ParallelConfig.distributed_executor_backend
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    # number of P/D disaggregation (or other disaggregation) workers
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    pipeline_parallel_size: int = ParallelConfig.pipeline_parallel_size
    tensor_parallel_size: int = ParallelConfig.tensor_parallel_size
    data_parallel_size: int = ParallelConfig.data_parallel_size
    enable_expert_parallel: bool = ParallelConfig.enable_expert_parallel
    max_parallel_loading_workers: Optional[
        int] = ParallelConfig.max_parallel_loading_workers
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    block_size: Optional[BlockSize] = CacheConfig.block_size
    enable_prefix_caching: Optional[bool] = CacheConfig.enable_prefix_caching
    prefix_caching_hash_algo: PrefixCachingHashAlgo = \
        CacheConfig.prefix_caching_hash_algo
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    disable_sliding_window: bool = ModelConfig.disable_sliding_window
    disable_cascade_attn: bool = ModelConfig.disable_cascade_attn
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    use_v2_block_manager: bool = True
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    swap_space: float = CacheConfig.swap_space
    cpu_offload_gb: float = CacheConfig.cpu_offload_gb
    gpu_memory_utilization: float = CacheConfig.gpu_memory_utilization
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    max_num_batched_tokens: Optional[
        int] = SchedulerConfig.max_num_batched_tokens
    max_num_partial_prefills: int = SchedulerConfig.max_num_partial_prefills
    max_long_partial_prefills: int = SchedulerConfig.max_long_partial_prefills
    long_prefill_token_threshold: int = \
        SchedulerConfig.long_prefill_token_threshold
    max_num_seqs: Optional[int] = SchedulerConfig.max_num_seqs
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    max_logprobs: int = ModelConfig.max_logprobs
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    disable_log_stats: bool = False
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    revision: Optional[str] = ModelConfig.revision
    code_revision: Optional[str] = ModelConfig.code_revision
    rope_scaling: dict[str, Any] = get_field(ModelConfig, "rope_scaling")
    rope_theta: Optional[float] = ModelConfig.rope_theta
    hf_token: Optional[Union[bool, str]] = ModelConfig.hf_token
    hf_overrides: Optional[HfOverrides] = \
        get_field(ModelConfig, "hf_overrides")
    tokenizer_revision: Optional[str] = ModelConfig.tokenizer_revision
    quantization: Optional[QuantizationMethods] = ModelConfig.quantization
    enforce_eager: bool = ModelConfig.enforce_eager
    max_seq_len_to_capture: int = ModelConfig.max_seq_len_to_capture
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    disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
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    # The following three fields are deprecated and will be removed in a future
    # release. Setting them will have no effect. Please remove them from your
    # configurations.
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    tokenizer_pool_size: int = TokenizerPoolConfig.pool_size
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    tokenizer_pool_type: str = TokenizerPoolConfig.pool_type
    tokenizer_pool_extra_config: dict = \
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        get_field(TokenizerPoolConfig, "extra_config")
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    limit_mm_per_prompt: dict[str, int] = \
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        get_field(MultiModalConfig, "limit_per_prompt")
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    mm_processor_kwargs: Optional[Dict[str, Any]] = \
        MultiModalConfig.mm_processor_kwargs
    disable_mm_preprocessor_cache: bool = \
        MultiModalConfig.disable_mm_preprocessor_cache
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    # LoRA fields
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    enable_lora: bool = False
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    enable_lora_bias: bool = LoRAConfig.bias_enabled
    max_loras: int = LoRAConfig.max_loras
    max_lora_rank: int = LoRAConfig.max_lora_rank
    fully_sharded_loras: bool = LoRAConfig.fully_sharded_loras
    max_cpu_loras: Optional[int] = LoRAConfig.max_cpu_loras
    lora_dtype: Optional[Union[str, torch.dtype]] = LoRAConfig.lora_dtype
    lora_extra_vocab_size: int = LoRAConfig.lora_extra_vocab_size
    long_lora_scaling_factors: Optional[tuple[float, ...]] = \
        LoRAConfig.long_lora_scaling_factors
    # PromptAdapter fields
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    enable_prompt_adapter: bool = False
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    max_prompt_adapters: int = PromptAdapterConfig.max_prompt_adapters
    max_prompt_adapter_token: int = \
        PromptAdapterConfig.max_prompt_adapter_token

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    device: Device = DeviceConfig.device
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    num_scheduler_steps: int = SchedulerConfig.num_scheduler_steps
    multi_step_stream_outputs: bool = SchedulerConfig.multi_step_stream_outputs
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    ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
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    num_gpu_blocks_override: Optional[
        int] = CacheConfig.num_gpu_blocks_override
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    num_lookahead_slots: int = SchedulerConfig.num_lookahead_slots
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    model_loader_extra_config: dict = \
        get_field(LoadConfig, "model_loader_extra_config")
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    ignore_patterns: Optional[Union[str,
                                    List[str]]] = LoadConfig.ignore_patterns
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    preemption_mode: Optional[str] = SchedulerConfig.preemption_mode
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    scheduler_delay_factor: float = SchedulerConfig.delay_factor
    enable_chunked_prefill: Optional[
        bool] = SchedulerConfig.enable_chunked_prefill
    disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input
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    guided_decoding_backend: GuidedDecodingBackend = DecodingConfig.backend
    guided_decoding_disable_fallback: bool = DecodingConfig.disable_fallback
    guided_decoding_disable_any_whitespace: bool = \
        DecodingConfig.disable_any_whitespace
    guided_decoding_disable_additional_properties: bool = \
        DecodingConfig.disable_additional_properties
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    logits_processor_pattern: Optional[
        str] = ModelConfig.logits_processor_pattern
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    speculative_config: Optional[Dict[str, Any]] = None
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    qlora_adapter_name_or_path: Optional[str] = None
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    show_hidden_metrics_for_version: Optional[str] = \
        ObservabilityConfig.show_hidden_metrics_for_version
    otlp_traces_endpoint: Optional[str] = \
        ObservabilityConfig.otlp_traces_endpoint
    collect_detailed_traces: Optional[list[DetailedTraceModules]] = \
        ObservabilityConfig.collect_detailed_traces
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    disable_async_output_proc: bool = not ModelConfig.use_async_output_proc
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    scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
    scheduler_cls: Union[str, Type[object]] = SchedulerConfig.scheduler_cls
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    override_neuron_config: dict[str, Any] = \
        get_field(ModelConfig, "override_neuron_config")
    override_pooler_config: Optional[Union[dict, PoolerConfig]] = \
        ModelConfig.override_pooler_config
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    compilation_config: Optional[CompilationConfig] = None
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    worker_cls: str = ParallelConfig.worker_cls
    worker_extension_cls: str = ParallelConfig.worker_extension_cls
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    kv_transfer_config: Optional[KVTransferConfig] = None
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    kv_events_config: Optional[KVEventsConfig] = None
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    generation_config: str = ModelConfig.generation_config
    enable_sleep_mode: bool = ModelConfig.enable_sleep_mode
    override_generation_config: dict[str, Any] = \
        get_field(ModelConfig, "override_generation_config")
    model_impl: str = ModelConfig.model_impl
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    calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
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    additional_config: Optional[Dict[str, Any]] = None
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    enable_reasoning: Optional[bool] = None  # DEPRECATED
    reasoning_parser: str = DecodingConfig.reasoning_backend

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    use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
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    pt_load_map_location: str = LoadConfig.pt_load_map_location
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    def __post_init__(self):
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        # support `EngineArgs(compilation_config={...})`
        # without having to manually construct a
        # CompilationConfig object
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        if isinstance(self.compilation_config, (int, dict)):
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            self.compilation_config = CompilationConfig.from_cli(
                str(self.compilation_config))
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        if self.qlora_adapter_name_or_path is not None:
            warnings.warn(
                "The `qlora_adapter_name_or_path` is deprecated "
                "and will be removed in v0.10.0. ",
                DeprecationWarning,
                stacklevel=2,
            )
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        # Setup plugins
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        from vllm.plugins import load_general_plugins
        load_general_plugins()
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    @staticmethod
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    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
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        """Shared CLI arguments for vLLM engine."""
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        # Model arguments
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        model_kwargs = get_kwargs(ModelConfig)
        model_group = parser.add_argument_group(
            title="ModelConfig",
            description=ModelConfig.__doc__,
        )
        model_group.add_argument("--model", **model_kwargs["model"])
        model_group.add_argument("--task", **model_kwargs["task"])
        model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
        model_group.add_argument("--tokenizer-mode",
                                 **model_kwargs["tokenizer_mode"])
        model_group.add_argument("--trust-remote-code",
                                 **model_kwargs["trust_remote_code"])
        model_group.add_argument("--dtype", **model_kwargs["dtype"])
        model_group.add_argument("--seed", **model_kwargs["seed"])
        model_group.add_argument("--hf-config-path",
                                 **model_kwargs["hf_config_path"])
        model_group.add_argument("--allowed-local-media-path",
                                 **model_kwargs["allowed_local_media_path"])
        model_group.add_argument("--revision", **model_kwargs["revision"])
        model_group.add_argument("--code-revision",
                                 **model_kwargs["code_revision"])
        model_group.add_argument("--rope-scaling",
                                 **model_kwargs["rope_scaling"])
        model_group.add_argument("--rope-theta", **model_kwargs["rope_theta"])
        model_group.add_argument("--tokenizer-revision",
                                 **model_kwargs["tokenizer_revision"])
        model_group.add_argument("--max-model-len",
                                 **model_kwargs["max_model_len"])
        model_group.add_argument("--quantization", "-q",
                                 **model_kwargs["quantization"])
        model_group.add_argument("--enforce-eager",
                                 **model_kwargs["enforce_eager"])
        model_group.add_argument("--max-seq-len-to-capture",
                                 **model_kwargs["max_seq_len_to_capture"])
        model_group.add_argument("--max-logprobs",
                                 **model_kwargs["max_logprobs"])
        model_group.add_argument("--disable-sliding-window",
                                 **model_kwargs["disable_sliding_window"])
        model_group.add_argument("--disable-cascade-attn",
                                 **model_kwargs["disable_cascade_attn"])
        model_group.add_argument("--skip-tokenizer-init",
                                 **model_kwargs["skip_tokenizer_init"])
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        model_group.add_argument("--enable-prompt-embeds",
                                 **model_kwargs["enable_prompt_embeds"])
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        model_group.add_argument("--served-model-name",
                                 **model_kwargs["served_model_name"])
        # This one is a special case because it is the
        # opposite of ModelConfig.use_async_output_proc
        model_group.add_argument(
            "--disable-async-output-proc",
            action="store_true",
            default=EngineArgs.disable_async_output_proc,
            help="Disable async output processing. This may result in "
            "lower performance.")
        model_group.add_argument("--config-format",
                                 choices=[f.value for f in ConfigFormat],
                                 **model_kwargs["config_format"])
        # This one is a special case because it can bool
        # or str. TODO: Handle this in get_kwargs
        model_group.add_argument("--hf-token",
                                 type=str,
                                 nargs="?",
                                 const=True,
                                 default=model_kwargs["hf_token"]["default"],
                                 help=model_kwargs["hf_token"]["help"])
        model_group.add_argument("--hf-overrides",
                                 **model_kwargs["hf_overrides"])
        model_group.add_argument("--override-neuron-config",
                                 **model_kwargs["override_neuron_config"])
        model_group.add_argument("--override-pooler-config",
                                 **model_kwargs["override_pooler_config"])
        model_group.add_argument("--logits-processor-pattern",
                                 **model_kwargs["logits_processor_pattern"])
        model_group.add_argument("--generation-config",
                                 **model_kwargs["generation_config"])
        model_group.add_argument("--override-generation-config",
                                 **model_kwargs["override_generation_config"])
        model_group.add_argument("--enable-sleep-mode",
                                 **model_kwargs["enable_sleep_mode"])
        model_group.add_argument("--model-impl",
                                 choices=[f.value for f in ModelImpl],
                                 **model_kwargs["model_impl"])

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        # Model loading arguments
        load_kwargs = get_kwargs(LoadConfig)
        load_group = parser.add_argument_group(
            title="LoadConfig",
            description=LoadConfig.__doc__,
        )
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        load_group.add_argument("--load-format",
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                                choices=[f.value for f in LoadFormat],
                                **load_kwargs["load_format"])
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        load_group.add_argument("--download-dir",
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                                **load_kwargs["download_dir"])
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        load_group.add_argument("--model-loader-extra-config",
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                                **load_kwargs["model_loader_extra_config"])
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        load_group.add_argument("--ignore-patterns",
                                **load_kwargs["ignore_patterns"])
        load_group.add_argument("--use-tqdm-on-load",
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                                **load_kwargs["use_tqdm_on_load"])
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        load_group.add_argument(
            "--qlora-adapter-name-or-path",
            type=str,
            default=None,
            help="The `--qlora-adapter-name-or-path` has no effect, do not set"
            " it, and it  will be removed in v0.10.0.",
            deprecated=True,
        )
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        load_group.add_argument('--pt-load-map-location',
                                **load_kwargs["pt_load_map_location"])
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        # Guided decoding arguments
        guided_decoding_kwargs = get_kwargs(DecodingConfig)
        guided_decoding_group = parser.add_argument_group(
            title="DecodingConfig",
            description=DecodingConfig.__doc__,
        )
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        guided_decoding_group.add_argument("--guided-decoding-backend",
                                           **guided_decoding_kwargs["backend"])
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        guided_decoding_group.add_argument(
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            "--guided-decoding-disable-fallback",
            **guided_decoding_kwargs["disable_fallback"])
        guided_decoding_group.add_argument(
            "--guided-decoding-disable-any-whitespace",
            **guided_decoding_kwargs["disable_any_whitespace"])
        guided_decoding_group.add_argument(
            "--guided-decoding-disable-additional-properties",
            **guided_decoding_kwargs["disable_additional_properties"])
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        guided_decoding_group.add_argument(
            "--enable-reasoning",
            action=argparse.BooleanOptionalAction,
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            deprecated=True,
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            help="[DEPRECATED] The `--enable-reasoning` flag is deprecated as "
            "of v0.8.6. Use `--reasoning-parser` to specify the reasoning "
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            "parser backend instead. This flag (`--enable-reasoning`) will be "
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            "removed in v0.10.0. When `--reasoning-parser` is specified, "
            "reasoning mode is automatically enabled.")
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        guided_decoding_group.add_argument(
            "--reasoning-parser",
            # This choices is a special case because it's not static
            choices=list(ReasoningParserManager.reasoning_parsers),
            **guided_decoding_kwargs["reasoning_backend"])

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        # Parallel arguments
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        parallel_kwargs = get_kwargs(ParallelConfig)
        parallel_group = parser.add_argument_group(
            title="ParallelConfig",
            description=ParallelConfig.__doc__,
        )
        parallel_group.add_argument(
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            "--distributed-executor-backend",
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            **parallel_kwargs["distributed_executor_backend"])
        parallel_group.add_argument(
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            "--pipeline-parallel-size", "-pp",
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            **parallel_kwargs["pipeline_parallel_size"])
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        parallel_group.add_argument("--tensor-parallel-size", "-tp",
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                                    **parallel_kwargs["tensor_parallel_size"])
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        parallel_group.add_argument("--data-parallel-size", "-dp",
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                                    **parallel_kwargs["data_parallel_size"])
        parallel_group.add_argument(
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            "--enable-expert-parallel",
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            **parallel_kwargs["enable_expert_parallel"])
        parallel_group.add_argument(
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            "--max-parallel-loading-workers",
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            **parallel_kwargs["max_parallel_loading_workers"])
        parallel_group.add_argument(
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            "--ray-workers-use-nsight",
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            **parallel_kwargs["ray_workers_use_nsight"])
        parallel_group.add_argument(
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            "--disable-custom-all-reduce",
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            **parallel_kwargs["disable_custom_all_reduce"])
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        parallel_group.add_argument("--worker-cls",
                                    **parallel_kwargs["worker_cls"])
        parallel_group.add_argument("--worker-extension-cls",
                                    **parallel_kwargs["worker_extension_cls"])
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        # KV cache arguments
        cache_kwargs = get_kwargs(CacheConfig)
        cache_group = parser.add_argument_group(
            title="CacheConfig",
            description=CacheConfig.__doc__,
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        )
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        cache_group.add_argument("--block-size", **cache_kwargs["block_size"])
        cache_group.add_argument("--gpu-memory-utilization",
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                                 **cache_kwargs["gpu_memory_utilization"])
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        cache_group.add_argument("--swap-space", **cache_kwargs["swap_space"])
        cache_group.add_argument("--kv-cache-dtype",
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                                 **cache_kwargs["cache_dtype"])
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        cache_group.add_argument("--num-gpu-blocks-override",
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                                 **cache_kwargs["num_gpu_blocks_override"])
        cache_group.add_argument("--enable-prefix-caching",
                                 **cache_kwargs["enable_prefix_caching"])
        cache_group.add_argument("--prefix-caching-hash-algo",
                                 **cache_kwargs["prefix_caching_hash_algo"])
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        cache_group.add_argument("--cpu-offload-gb",
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                                 **cache_kwargs["cpu_offload_gb"])
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        cache_group.add_argument("--calculate-kv-scales",
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                                 **cache_kwargs["calculate_kv_scales"])

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        # Tokenizer arguments
        tokenizer_kwargs = get_kwargs(TokenizerPoolConfig)
        tokenizer_group = parser.add_argument_group(
            title="TokenizerPoolConfig",
            description=TokenizerPoolConfig.__doc__,
        )
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        tokenizer_group.add_argument("--tokenizer-pool-size",
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                                     **tokenizer_kwargs["pool_size"])
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        tokenizer_group.add_argument("--tokenizer-pool-type",
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                                     **tokenizer_kwargs["pool_type"])
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        tokenizer_group.add_argument("--tokenizer-pool-extra-config",
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                                     **tokenizer_kwargs["extra_config"])
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        # Multimodal related configs
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        multimodal_kwargs = get_kwargs(MultiModalConfig)
        multimodal_group = parser.add_argument_group(
            title="MultiModalConfig",
            description=MultiModalConfig.__doc__,
        )
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        multimodal_group.add_argument("--limit-mm-per-prompt",
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                                      **multimodal_kwargs["limit_per_prompt"])
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        multimodal_group.add_argument(
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            "--mm-processor-kwargs",
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            **multimodal_kwargs["mm_processor_kwargs"])
        multimodal_group.add_argument(
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            "--disable-mm-preprocessor-cache",
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            **multimodal_kwargs["disable_mm_preprocessor_cache"])
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        # LoRA related configs
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        lora_kwargs = get_kwargs(LoRAConfig)
        lora_group = parser.add_argument_group(
            title="LoRAConfig",
            description=LoRAConfig.__doc__,
        )
        lora_group.add_argument(
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            "--enable-lora",
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            action=argparse.BooleanOptionalAction,
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            help="If True, enable handling of LoRA adapters.")
        lora_group.add_argument("--enable-lora-bias",
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                                **lora_kwargs["bias_enabled"])
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        lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
        lora_group.add_argument("--max-lora-rank",
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                                **lora_kwargs["max_lora_rank"])
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        lora_group.add_argument("--lora-extra-vocab-size",
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                                **lora_kwargs["lora_extra_vocab_size"])
        lora_group.add_argument(
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            "--lora-dtype",
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            **lora_kwargs["lora_dtype"],
        )
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        lora_group.add_argument("--long-lora-scaling-factors",
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                                **lora_kwargs["long_lora_scaling_factors"])
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        lora_group.add_argument("--max-cpu-loras",
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                                **lora_kwargs["max_cpu_loras"])
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        lora_group.add_argument("--fully-sharded-loras",
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                                **lora_kwargs["fully_sharded_loras"])

        # PromptAdapter related configs
        prompt_adapter_kwargs = get_kwargs(PromptAdapterConfig)
        prompt_adapter_group = parser.add_argument_group(
            title="PromptAdapterConfig",
            description=PromptAdapterConfig.__doc__,
        )
        prompt_adapter_group.add_argument(
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            "--enable-prompt-adapter",
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            action=argparse.BooleanOptionalAction,
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            help="If True, enable handling of PromptAdapters.")
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        prompt_adapter_group.add_argument(
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            "--max-prompt-adapters",
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            **prompt_adapter_kwargs["max_prompt_adapters"])
        prompt_adapter_group.add_argument(
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            "--max-prompt-adapter-token",
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            **prompt_adapter_kwargs["max_prompt_adapter_token"])
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        # Device arguments
        device_kwargs = get_kwargs(DeviceConfig)
        device_group = parser.add_argument_group(
            title="DeviceConfig",
            description=DeviceConfig.__doc__,
        )
        device_group.add_argument("--device", **device_kwargs["device"])

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        # Speculative arguments
        speculative_group = parser.add_argument_group(
            title="SpeculativeConfig",
            description=SpeculativeConfig.__doc__,
        )
        speculative_group.add_argument(
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            "--speculative-config",
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            type=json.loads,
            default=None,
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            help="The configurations for speculative decoding. Should be a "
            "JSON string.")
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        # Observability arguments
        observability_kwargs = get_kwargs(ObservabilityConfig)
        observability_group = parser.add_argument_group(
            title="ObservabilityConfig",
            description=ObservabilityConfig.__doc__,
        )
        observability_group.add_argument(
            "--show-hidden-metrics-for-version",
            **observability_kwargs["show_hidden_metrics_for_version"])
        observability_group.add_argument(
            "--otlp-traces-endpoint",
            **observability_kwargs["otlp_traces_endpoint"])
        # TODO: generalise this special case
        choices = observability_kwargs["collect_detailed_traces"]["choices"]
        metavar = f"{{{','.join(choices)}}}"
        observability_kwargs["collect_detailed_traces"]["metavar"] = metavar
        observability_kwargs["collect_detailed_traces"]["choices"] += [
            ",".join(p)
            for p in permutations(get_args(DetailedTraceModules), r=2)
        ]
        observability_group.add_argument(
            "--collect-detailed-traces",
            **observability_kwargs["collect_detailed_traces"])
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        # Scheduler arguments
        scheduler_kwargs = get_kwargs(SchedulerConfig)
        scheduler_group = parser.add_argument_group(
            title="SchedulerConfig",
            description=SchedulerConfig.__doc__,
        )
        scheduler_group.add_argument(
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            "--max-num-batched-tokens",
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            **scheduler_kwargs["max_num_batched_tokens"])
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        scheduler_group.add_argument("--max-num-seqs",
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                                     **scheduler_kwargs["max_num_seqs"])
        scheduler_group.add_argument(
            "--max-num-partial-prefills",
            **scheduler_kwargs["max_num_partial_prefills"])
        scheduler_group.add_argument(
            "--max-long-partial-prefills",
            **scheduler_kwargs["max_long_partial_prefills"])
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        scheduler_group.add_argument('--cuda-graph-sizes',
                                     **scheduler_kwargs["cuda_graph_sizes"])
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        scheduler_group.add_argument(
            "--long-prefill-token-threshold",
            **scheduler_kwargs["long_prefill_token_threshold"])
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        scheduler_group.add_argument("--num-lookahead-slots",
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                                     **scheduler_kwargs["num_lookahead_slots"])
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        scheduler_group.add_argument("--scheduler-delay-factor",
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                                     **scheduler_kwargs["delay_factor"])
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        scheduler_group.add_argument("--preemption-mode",
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                                     **scheduler_kwargs["preemption_mode"])
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        scheduler_group.add_argument("--num-scheduler-steps",
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                                     **scheduler_kwargs["num_scheduler_steps"])
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        scheduler_group.add_argument(
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            "--multi-step-stream-outputs",
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            **scheduler_kwargs["multi_step_stream_outputs"])
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        scheduler_group.add_argument("--scheduling-policy",
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                                     **scheduler_kwargs["policy"])
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        scheduler_group.add_argument(
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            "--enable-chunked-prefill",
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            **scheduler_kwargs["enable_chunked_prefill"])
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        scheduler_group.add_argument(
            "--disable-chunked-mm-input",
            **scheduler_kwargs["disable_chunked_mm_input"])
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        scheduler_group.add_argument("--scheduler-cls",
                                     **scheduler_kwargs["scheduler_cls"])

        # vLLM arguments
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        vllm_kwargs = get_kwargs(VllmConfig)
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        vllm_group = parser.add_argument_group(
            title="VllmConfig",
            description=VllmConfig.__doc__,
        )
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        vllm_group.add_argument("--kv-transfer-config",
                                **vllm_kwargs["kv_transfer_config"])
        vllm_group.add_argument('--kv-events-config',
                                **vllm_kwargs["kv_events_config"])
        vllm_group.add_argument("--compilation-config", "-O",
                                **vllm_kwargs["compilation_config"])
        vllm_group.add_argument("--additional-config",
                                **vllm_kwargs["additional_config"])
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        # Other arguments
        parser.add_argument('--use-v2-block-manager',
                            action='store_true',
                            default=True,
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                            deprecated=True,
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                            help='[DEPRECATED] block manager v1 has been '
                            'removed and SelfAttnBlockSpaceManager (i.e. '
                            'block manager v2) is now the default. '
                            'Setting this flag to True or False'
                            ' has no effect on vLLM behavior.')
        parser.add_argument('--disable-log-stats',
                            action='store_true',
                            help='Disable logging statistics.')
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        return parser
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    @classmethod
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    def from_cli_args(cls, args: argparse.Namespace):
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        # Get the list of attributes of this dataclass.
        attrs = [attr.name for attr in dataclasses.fields(cls)]
        # Set the attributes from the parsed arguments.
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        engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
        return engine_args
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    def create_model_config(self) -> ModelConfig:
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        # gguf file needs a specific model loader and doesn't use hf_repo
        if check_gguf_file(self.model):
            self.quantization = self.load_format = "gguf"

        # NOTE: This is to allow model loading from S3 in CI
        if (not isinstance(self, AsyncEngineArgs) and envs.VLLM_CI_USE_S3
                and self.model in MODELS_ON_S3
                and self.load_format == LoadFormat.AUTO):  # noqa: E501
            self.model = f"{MODEL_WEIGHTS_S3_BUCKET}/{self.model}"
            self.load_format = LoadFormat.RUNAI_STREAMER

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        return ModelConfig(
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            model=self.model,
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            hf_config_path=self.hf_config_path,
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            task=self.task,
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            tokenizer=self.tokenizer,
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            tokenizer_mode=self.tokenizer_mode,
            trust_remote_code=self.trust_remote_code,
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            allowed_local_media_path=self.allowed_local_media_path,
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            dtype=self.dtype,
            seed=self.seed,
            revision=self.revision,
            code_revision=self.code_revision,
            rope_scaling=self.rope_scaling,
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            rope_theta=self.rope_theta,
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            hf_token=self.hf_token,
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            hf_overrides=self.hf_overrides,
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            tokenizer_revision=self.tokenizer_revision,
            max_model_len=self.max_model_len,
            quantization=self.quantization,
            enforce_eager=self.enforce_eager,
            max_seq_len_to_capture=self.max_seq_len_to_capture,
            max_logprobs=self.max_logprobs,
            disable_sliding_window=self.disable_sliding_window,
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            disable_cascade_attn=self.disable_cascade_attn,
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            skip_tokenizer_init=self.skip_tokenizer_init,
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            enable_prompt_embeds=self.enable_prompt_embeds,
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            served_model_name=self.served_model_name,
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            limit_mm_per_prompt=self.limit_mm_per_prompt,
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            use_async_output_proc=not self.disable_async_output_proc,
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            config_format=self.config_format,
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            mm_processor_kwargs=self.mm_processor_kwargs,
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            disable_mm_preprocessor_cache=self.disable_mm_preprocessor_cache,
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            override_neuron_config=self.override_neuron_config,
            override_pooler_config=self.override_pooler_config,
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            logits_processor_pattern=self.logits_processor_pattern,
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            generation_config=self.generation_config,
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            override_generation_config=self.override_generation_config,
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            enable_sleep_mode=self.enable_sleep_mode,
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            model_impl=self.model_impl,
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        )
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    def create_load_config(self) -> LoadConfig:

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        if self.quantization == "bitsandbytes":
            self.load_format = "bitsandbytes"
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        return LoadConfig(
            load_format=self.load_format,
            download_dir=self.download_dir,
            model_loader_extra_config=self.model_loader_extra_config,
            ignore_patterns=self.ignore_patterns,
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            use_tqdm_on_load=self.use_tqdm_on_load,
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            pt_load_map_location=self.pt_load_map_location,
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        )
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    def create_speculative_config(
        self,
        target_model_config: ModelConfig,
        target_parallel_config: ParallelConfig,
        enable_chunked_prefill: bool,
        disable_log_stats: bool,
    ) -> Optional["SpeculativeConfig"]:
        """Initializes and returns a SpeculativeConfig object based on
        `speculative_config`.

        This function utilizes `speculative_config` to create a
        SpeculativeConfig object. The `speculative_config` can either be
        provided as a JSON string input via CLI arguments or directly as a
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        dictionary from the engine.
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        """
        if self.speculative_config is None:
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            return None

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        # Note(Shangming): These parameters are not obtained from the cli arg
        # '--speculative-config' and must be passed in when creating the engine
        # config.
        self.speculative_config.update({
            "target_model_config": target_model_config,
            "target_parallel_config": target_parallel_config,
            "enable_chunked_prefill": enable_chunked_prefill,
            "disable_log_stats": disable_log_stats,
        })
        speculative_config = SpeculativeConfig.from_dict(
            self.speculative_config)

        return speculative_config

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    def create_engine_config(
        self,
        usage_context: Optional[UsageContext] = None,
    ) -> VllmConfig:
        """
        Create the VllmConfig.

        NOTE: for autoselection of V0 vs V1 engine, we need to
        create the ModelConfig first, since ModelConfig's attrs
        (e.g. the model arch) are needed to make the decision.
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        This function set VLLM_USE_V1=X if VLLM_USE_V1 is
        unspecified by the user.

        If VLLM_USE_V1 is specified by the user but the VllmConfig
        is incompatible, we raise an error.
        """
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        from vllm.platforms import current_platform
        current_platform.pre_register_and_update()
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        device_config = DeviceConfig(device=self.device)
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        model_config = self.create_model_config()

959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
        # * If VLLM_USE_V1 is unset, we enable V1 for "supported features"
        #   and fall back to V0 for experimental or unsupported features.
        # * If VLLM_USE_V1=1, we enable V1 for supported + experimental
        #   features and raise error for unsupported features.
        # * If VLLM_USE_V1=0, we disable V1.
        use_v1 = False
        try_v1 = envs.VLLM_USE_V1 or not envs.is_set("VLLM_USE_V1")
        if try_v1 and self._is_v1_supported_oracle(model_config):
            use_v1 = True

        # If user explicitly set VLLM_USE_V1, sanity check we respect it.
        if envs.is_set("VLLM_USE_V1"):
            assert use_v1 == envs.VLLM_USE_V1
        # Otherwise, set the VLLM_USE_V1 variable globally.
        else:
            envs.set_vllm_use_v1(use_v1)

        # Set default arguments for V0 or V1 Engine.
        if use_v1:
            self._set_default_args_v1(usage_context)
        else:
            self._set_default_args_v0(model_config)
981

982
983
        assert self.enable_chunked_prefill is not None

984
        cache_config = CacheConfig(
985
            block_size=self.block_size,
986
987
988
            gpu_memory_utilization=self.gpu_memory_utilization,
            swap_space=self.swap_space,
            cache_dtype=self.kv_cache_dtype,
989
            is_attention_free=model_config.is_attention_free,
990
991
            num_gpu_blocks_override=self.num_gpu_blocks_override,
            sliding_window=model_config.get_sliding_window(),
992
            enable_prefix_caching=self.enable_prefix_caching,
993
            prefix_caching_hash_algo=self.prefix_caching_hash_algo,
994
            cpu_offload_gb=self.cpu_offload_gb,
995
            calculate_kv_scales=self.calculate_kv_scales,
996
        )
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008

        # Get the current placement group if Ray is initialized and
        # we are in a Ray actor. If so, then the placement group will be
        # passed to spawned processes.
        placement_group = None
        if is_in_ray_actor():
            import ray

            # This call initializes Ray automatically if it is not initialized,
            # but we should not do this here.
            placement_group = ray.util.get_current_placement_group()

1009
        parallel_config = ParallelConfig(
1010
1011
            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
1012
            data_parallel_size=self.data_parallel_size,
1013
            enable_expert_parallel=self.enable_expert_parallel,
1014
1015
1016
            max_parallel_loading_workers=self.max_parallel_loading_workers,
            disable_custom_all_reduce=self.disable_custom_all_reduce,
            ray_workers_use_nsight=self.ray_workers_use_nsight,
1017
            placement_group=placement_group,
1018
1019
            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
1020
            worker_extension_cls=self.worker_extension_cls,
1021
        )
1022

1023
        speculative_config = self.create_speculative_config(
1024
1025
            target_model_config=model_config,
            target_parallel_config=parallel_config,
1026
            enable_chunked_prefill=self.enable_chunked_prefill,
1027
            disable_log_stats=self.disable_log_stats,
1028
1029
        )

1030
        # Reminder: Please update docs/source/features/compatibility_matrix.md
1031
        # If the feature combo become valid
1032
1033
1034
1035
        if self.num_scheduler_steps > 1:
            if speculative_config is not None:
                raise ValueError("Speculative decoding is not supported with "
                                 "multi-step (--num-scheduler-steps > 1)")
1036
1037
1038
            if self.enable_chunked_prefill and self.pipeline_parallel_size > 1:
                raise ValueError("Multi-Step Chunked-Prefill is not supported "
                                 "for pipeline-parallel-size > 1")
1039
1040
1041
1042
1043
1044
            from vllm.platforms import current_platform
            if current_platform.is_cpu():
                logger.warning("Multi-Step (--num-scheduler-steps > 1) is "
                               "currently not supported for CPUs and has been "
                               "disabled.")
                self.num_scheduler_steps = 1
1045
1046
1047
1048
1049
1050
1051
1052
1053

        # make sure num_lookahead_slots is set the higher value depending on
        # if we are using speculative decoding or multi-step
        num_lookahead_slots = max(self.num_lookahead_slots,
                                  self.num_scheduler_steps - 1)
        num_lookahead_slots = num_lookahead_slots \
            if speculative_config is None \
            else speculative_config.num_lookahead_slots

1054
        scheduler_config = SchedulerConfig(
1055
            runner_type=model_config.runner_type,
1056
1057
1058
            max_num_batched_tokens=self.max_num_batched_tokens,
            max_num_seqs=self.max_num_seqs,
            max_model_len=model_config.max_model_len,
1059
            cuda_graph_sizes=self.cuda_graph_sizes,
1060
            num_lookahead_slots=num_lookahead_slots,
1061
1062
            delay_factor=self.scheduler_delay_factor,
            enable_chunked_prefill=self.enable_chunked_prefill,
1063
            disable_chunked_mm_input=self.disable_chunked_mm_input,
1064
            is_multimodal_model=model_config.is_multimodal_model,
1065
            preemption_mode=self.preemption_mode,
1066
            num_scheduler_steps=self.num_scheduler_steps,
1067
            multi_step_stream_outputs=self.multi_step_stream_outputs,
1068
1069
            send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
                             and parallel_config.use_ray),
1070
            policy=self.scheduling_policy,
1071
            scheduler_cls=self.scheduler_cls,
1072
1073
1074
1075
            max_num_partial_prefills=self.max_num_partial_prefills,
            max_long_partial_prefills=self.max_long_partial_prefills,
            long_prefill_token_threshold=self.long_prefill_token_threshold,
        )
1076

1077
        lora_config = LoRAConfig(
1078
            bias_enabled=self.enable_lora_bias,
1079
1080
            max_lora_rank=self.max_lora_rank,
            max_loras=self.max_loras,
1081
            fully_sharded_loras=self.fully_sharded_loras,
1082
            lora_extra_vocab_size=self.lora_extra_vocab_size,
1083
            long_lora_scaling_factors=self.long_lora_scaling_factors,
1084
1085
1086
            lora_dtype=self.lora_dtype,
            max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras
            and self.max_cpu_loras > 0 else None) if self.enable_lora else None
1087

1088
1089
1090
1091
        # bitsandbytes pre-quantized model need a specific model loader
        if model_config.quantization == "bitsandbytes":
            self.quantization = self.load_format = "bitsandbytes"

1092
        load_config = self.create_load_config()
1093

1094
1095
1096
1097
1098
        prompt_adapter_config = PromptAdapterConfig(
            max_prompt_adapters=self.max_prompt_adapters,
            max_prompt_adapter_token=self.max_prompt_adapter_token) \
                                        if self.enable_prompt_adapter else None

1099
        decoding_config = DecodingConfig(
1100
1101
1102
1103
1104
            backend=self.guided_decoding_backend,
            disable_fallback=self.guided_decoding_disable_fallback,
            disable_any_whitespace=self.guided_decoding_disable_any_whitespace,
            disable_additional_properties=\
                self.guided_decoding_disable_additional_properties,
1105
1106
            reasoning_backend=self.reasoning_parser
        )
1107

1108
        observability_config = ObservabilityConfig(
1109
1110
            show_hidden_metrics_for_version=self.
            show_hidden_metrics_for_version,
1111
            otlp_traces_endpoint=self.otlp_traces_endpoint,
1112
            collect_detailed_traces=self.collect_detailed_traces,
1113
        )
1114

1115
        config = VllmConfig(
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
            model_config=model_config,
            cache_config=cache_config,
            parallel_config=parallel_config,
            scheduler_config=scheduler_config,
            device_config=device_config,
            lora_config=lora_config,
            speculative_config=speculative_config,
            load_config=load_config,
            decoding_config=decoding_config,
            observability_config=observability_config,
1126
            prompt_adapter_config=prompt_adapter_config,
1127
            compilation_config=self.compilation_config,
1128
            kv_transfer_config=self.kv_transfer_config,
1129
            kv_events_config=self.kv_events_config,
1130
            additional_config=self.additional_config,
1131
        )
1132

1133
1134
        return config

1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
    def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
        """Oracle for whether to use V0 or V1 Engine by default."""

        #############################################################
        # Unsupported Feature Flags on V1.

        if (self.load_format == LoadFormat.TENSORIZER.value
                or self.load_format == LoadFormat.SHARDED_STATE.value):
            _raise_or_fallback(
                feature_name=f"--load_format {self.load_format}",
                recommend_to_remove=False)
            return False

        if (self.logits_processor_pattern
                != EngineArgs.logits_processor_pattern):
            _raise_or_fallback(feature_name="--logits-processor-pattern",
                               recommend_to_remove=False)
            return False

1154
        if self.preemption_mode != SchedulerConfig.preemption_mode:
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
            _raise_or_fallback(feature_name="--preemption-mode",
                               recommend_to_remove=True)
            return False

        if (self.disable_async_output_proc
                != EngineArgs.disable_async_output_proc):
            _raise_or_fallback(feature_name="--disable-async-output-proc",
                               recommend_to_remove=True)
            return False

1165
        if self.scheduling_policy != SchedulerConfig.policy:
1166
1167
1168
1169
            _raise_or_fallback(feature_name="--scheduling-policy",
                               recommend_to_remove=False)
            return False

1170
        if self.num_scheduler_steps != SchedulerConfig.num_scheduler_steps:
1171
1172
1173
1174
            _raise_or_fallback(feature_name="--num-scheduler-steps",
                               recommend_to_remove=True)
            return False

1175
        if self.scheduler_delay_factor != SchedulerConfig.delay_factor:
1176
1177
1178
1179
            _raise_or_fallback(feature_name="--scheduler-delay-factor",
                               recommend_to_remove=True)
            return False

1180
1181
        if self.guided_decoding_backend not in get_args(
                GuidedDecodingBackendV1):
1182
1183
1184
1185
            _raise_or_fallback(
                feature_name=
                f"--guided-decoding-backend={self.guided_decoding_backend}",
                recommend_to_remove=False)
1186
1187
1188
            return False

        # Need at least Ampere for now (FA support required).
1189
1190
1191
        # Skip this check if we are running on a non-GPU platform,
        # or if the device capability is not available
        # (e.g. in a Ray actor without GPUs).
1192
1193
        from vllm.platforms import current_platform
        if (current_platform.is_cuda()
1194
                and current_platform.get_device_capability()
1195
1196
1197
1198
1199
1200
1201
                and current_platform.get_device_capability().major < 8):
            _raise_or_fallback(feature_name="Compute Capability < 8.0",
                               recommend_to_remove=False)
            return False

        # No Fp8 KV cache so far.
        if self.kv_cache_dtype != "auto":
1202
1203
1204
1205
1206
1207
            fp8_attention = self.kv_cache_dtype.startswith("fp8")
            will_use_fa = (
                current_platform.is_cuda()
                and not envs.is_set("VLLM_ATTENTION_BACKEND")
            ) or envs.VLLM_ATTENTION_BACKEND == "FLASH_ATTN_VLLM_V1"
            supported = False
1208
1209
1210
            if current_platform.is_rocm():
                supported = True
            elif fp8_attention and will_use_fa:
1211
                from vllm.attention.utils.fa_utils import (
1212
1213
1214
1215
1216
1217
                    flash_attn_supports_fp8)
                supported = flash_attn_supports_fp8()
            if not supported:
                _raise_or_fallback(feature_name="--kv-cache-dtype",
                                   recommend_to_remove=False)
                return False
1218
1219
1220
1221
1222
1223
1224

        # No Prompt Adapter so far.
        if self.enable_prompt_adapter:
            _raise_or_fallback(feature_name="--enable-prompt-adapter",
                               recommend_to_remove=False)
            return False

1225
1226
1227
1228
1229
1230
        # No text embedding inputs so far.
        if self.enable_prompt_embeds:
            _raise_or_fallback(feature_name="--enable-prompt-embeds",
                               recommend_to_remove=False)
            return False

1231
1232
1233
1234
1235
1236
1237
1238
        # Only Fp16 and Bf16 dtypes since we only support FA.
        V1_SUPPORTED_DTYPES = [torch.bfloat16, torch.float16]
        if model_config.dtype not in V1_SUPPORTED_DTYPES:
            _raise_or_fallback(feature_name=f"--dtype {model_config.dtype}",
                               recommend_to_remove=False)
            return False

        # Some quantization is not compatible with torch.compile.
1239
        V1_UNSUPPORTED_QUANT = ["gguf"]
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
        if model_config.quantization in V1_UNSUPPORTED_QUANT:
            _raise_or_fallback(
                feature_name=f"--quantization {model_config.quantization}",
                recommend_to_remove=False)
            return False

        # No Embedding Models so far.
        if model_config.task not in ["generate"]:
            _raise_or_fallback(feature_name=f"--task {model_config.task}",
                               recommend_to_remove=False)
            return False

        # No Mamba or Encoder-Decoder so far.
        if not model_config.is_v1_compatible:
            _raise_or_fallback(feature_name=model_config.architectures,
                               recommend_to_remove=False)
            return False

        # No Concurrent Partial Prefills so far.
        if (self.max_num_partial_prefills
1260
                != SchedulerConfig.max_num_partial_prefills
1261
                or self.max_long_partial_prefills
1262
                != SchedulerConfig.max_long_partial_prefills):
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
            _raise_or_fallback(feature_name="Concurrent Partial Prefill",
                               recommend_to_remove=False)
            return False

        # No OTLP observability so far.
        if (self.otlp_traces_endpoint or self.collect_detailed_traces):
            _raise_or_fallback(feature_name="--otlp-traces-endpoint",
                               recommend_to_remove=False)
            return False

        # Only Ngram speculative decoding so far.
1274
        is_ngram_enabled = False
1275
        is_eagle_enabled = False
1276
        if self.speculative_config is not None:
1277
            # This is supported but experimental (handled below).
1278
1279
1280
1281
            speculative_method = self.speculative_config.get("method")
            if speculative_method:
                if speculative_method in ("ngram", "[ngram]"):
                    is_ngram_enabled = True
1282
                elif speculative_method in ("eagle", "eagle3"):
1283
                    is_eagle_enabled = True
1284
            else:
1285
1286
1287
1288
1289
                speculative_model = self.speculative_config.get("model")
                if speculative_model in ("ngram", "[ngram]"):
                    is_ngram_enabled = True
            if not (is_ngram_enabled or is_eagle_enabled):
                # Other speculative decoding methods are not supported yet.
1290
1291
1292
1293
                _raise_or_fallback(feature_name="Speculative Decoding",
                                   recommend_to_remove=False)
                return False

1294
        # No XFormers so far.
1295
        V1_BACKENDS = [
1296
1297
1298
1299
1300
1301
1302
1303
1304
            "FLASH_ATTN_VLLM_V1",
            "FLASH_ATTN",
            "PALLAS",
            "PALLAS_VLLM_V1",
            "TRITON_ATTN_VLLM_V1",
            "TRITON_MLA",
            "FLASHMLA",
            "FLASHINFER",
            "FLASHINFER_VLLM_V1",
1305
            "ROCM_AITER_MLA",
1306
1307
1308
1309
1310
1311
1312
        ]
        if (envs.is_set("VLLM_ATTENTION_BACKEND")
                and envs.VLLM_ATTENTION_BACKEND not in V1_BACKENDS):
            name = f"VLLM_ATTENTION_BACKEND={envs.VLLM_ATTENTION_BACKEND}"
            _raise_or_fallback(feature_name=name, recommend_to_remove=True)
            return False

1313
1314
        # Platforms must decide if they can support v1 for this model
        if not current_platform.supports_v1(model_config=model_config):
1315
1316
1317
1318
            _raise_or_fallback(
                feature_name=f"device type={current_platform.device_type}",
                recommend_to_remove=False)
            return False
1319
1320
1321
        #############################################################
        # Experimental Features - allow users to opt in.

1322
1323
1324
1325
1326
        # Signal Handlers requires running in main thread.
        if (threading.current_thread() != threading.main_thread()
                and _warn_or_fallback("Engine in background thread")):
            return False

1327
        if (self.pipeline_parallel_size > 1
1328
1329
1330
                and self.distributed_executor_backend not in ["ray", "mp"]):
            name = "Pipeline Parallelism without Ray distributed executor " \
                    "or multiprocessing executor"
1331
            _raise_or_fallback(feature_name=name, recommend_to_remove=False)
1332
1333
1334
            return False

        # ngram is supported on V1, but off by default for now.
1335
        if is_ngram_enabled and _warn_or_fallback("ngram"):
1336
1337
            return False

1338
1339
1340
1341
        # Eagle is under development, so we don't support it yet.
        if is_eagle_enabled and _warn_or_fallback("Eagle"):
            return False

1342
1343
1344
1345
        # Non-[CUDA, TPU] may be supported on V1, but off by default for now.
        v0_hardware = not any(
            (current_platform.is_cuda(), current_platform.is_tpu()))
        if v0_hardware and _warn_or_fallback(  # noqa: SIM103
1346
                current_platform.device_name):
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
            return False
        #############################################################

        return True

    def _set_default_args_v0(self, model_config: ModelConfig) -> None:
        """Set Default Arguments for V0 Engine."""

        max_model_len = model_config.max_model_len
        use_long_context = max_model_len > 32768
        if self.enable_chunked_prefill is None:
            # Chunked prefill not supported for Multimodal or MLA in V0.
            if model_config.is_multimodal_model or model_config.use_mla:
                self.enable_chunked_prefill = False

            # Enable chunked prefill by default for long context (> 32K)
            # models to avoid OOM errors in initial memory profiling phase.
            elif use_long_context:
                from vllm.platforms import current_platform
                is_gpu = current_platform.is_cuda()
                use_sliding_window = (model_config.get_sliding_window()
                                      is not None)
1369
                use_spec_decode = self.speculative_config is not None
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396

                if (is_gpu and not use_sliding_window and not use_spec_decode
                        and not self.enable_lora
                        and not self.enable_prompt_adapter
                        and model_config.runner_type != "pooling"):
                    self.enable_chunked_prefill = True
                    logger.warning(
                        "Chunked prefill is enabled by default for models "
                        "with max_model_len > 32K. Chunked prefill might "
                        "not work with some features or models. If you "
                        "encounter any issues, please disable by launching "
                        "with --enable-chunked-prefill=False.")

            if self.enable_chunked_prefill is None:
                self.enable_chunked_prefill = False

        if not self.enable_chunked_prefill and use_long_context:
            logger.warning(
                "The model has a long context length (%s). This may cause"
                "OOM during the initial memory profiling phase, or result "
                "in low performance due to small KV cache size. Consider "
                "setting --max-model-len to a smaller value.", max_model_len)
        elif (self.enable_chunked_prefill
              and model_config.runner_type == "pooling"):
            msg = "Chunked prefill is not supported for pooling models"
            raise ValueError(msg)

1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
        # if using prefix caching, we must set a hash algo
        if self.enable_prefix_caching:
            # Disable prefix caching for multimodal models for VLLM_V0.
            if model_config.is_multimodal_model:
                logger.warning(
                    "--enable-prefix-caching is not supported for multimodal "
                    "models in V0 and has been disabled.")
                self.enable_prefix_caching = False

            # VLLM_V0 only supports builtin hash algo for prefix caching.
1407
            if self.prefix_caching_hash_algo == "sha256":
1408
1409
1410
                raise ValueError(
                    "sha256 is not supported for prefix caching in V0 engine. "
                    "Please use 'builtin'.")
1411
1412
1413
1414
1415
1416
1417

        # Set max_num_seqs to 256 for VLLM_V0.
        if self.max_num_seqs is None:
            self.max_num_seqs = 256

    def _set_default_args_v1(self, usage_context: UsageContext) -> None:
        """Set Default Arguments for V1 Engine."""
1418

1419
1420
        # V1 always uses chunked prefills.
        self.enable_chunked_prefill = True
1421
1422
1423
1424
1425

        # V1 enables prefix caching by default.
        if self.enable_prefix_caching is None:
            self.enable_prefix_caching = True

1426
1427
1428
        # V1 should use the new scheduler by default.
        # Swap it only if this arg is set to the original V0 default
        if self.scheduler_cls == EngineArgs.scheduler_cls:
1429
            self.scheduler_cls = "vllm.v1.core.sched.scheduler.Scheduler"
1430

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        # When no user override, set the default values based on the usage
        # context.
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        # Use different default values for different hardware.
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        # Try to query the device name on the current platform. If it fails,
        # it may be because the platform that imports vLLM is not the same
        # as the platform that vLLM is running on (e.g. the case of scaling
        # vLLM with Ray) and has no GPUs. In this case we use the default
        # values for non-H100/H200 GPUs.
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        from vllm.platforms import current_platform
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        try:
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            device_memory = current_platform.get_device_total_memory()
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            device_name = current_platform.get_device_name().lower()
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        except Exception:
            # This is only used to set default_max_num_batched_tokens
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            device_memory = 0
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        # NOTE(Kuntai): Setting large `max_num_batched_tokens` for A100 reduces
        # throughput, see PR #17885 for more details.
        # So here we do an extra device name check to prevent such regression.
        if device_memory >= 70 * GiB_bytes and "a100" not in device_name:
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            # For GPUs like H100 and MI300x, use larger default values.
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            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 16384,
                UsageContext.OPENAI_API_SERVER: 8192,
            }
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            default_max_num_seqs = 1024
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        else:
            # TODO(woosuk): Tune the default values for other hardware.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 8192,
                UsageContext.OPENAI_API_SERVER: 2048,
            }
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            default_max_num_seqs = 256
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        # tpu specific default values.
        if current_platform.is_tpu():
            default_max_num_batched_tokens_tpu = {
                UsageContext.LLM_CLASS: {
                    'V6E': 2048,
                    'V5E': 1024,
                    'V5P': 512,
                },
                UsageContext.OPENAI_API_SERVER: {
                    'V6E': 1024,
                    'V5E': 512,
                    'V5P': 256,
                }
            }

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        use_context_value = usage_context.value if usage_context else None
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        if (self.max_num_batched_tokens is None
                and usage_context in default_max_num_batched_tokens):
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            if current_platform.is_tpu():
                chip_name = current_platform.get_device_name()
                if chip_name in default_max_num_batched_tokens_tpu[
                        usage_context]:
                    self.max_num_batched_tokens = \
                        default_max_num_batched_tokens_tpu[
                            usage_context][chip_name]
                else:
                    self.max_num_batched_tokens = \
                        default_max_num_batched_tokens[usage_context]
            else:
                self.max_num_batched_tokens = default_max_num_batched_tokens[
                    usage_context]
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            logger.debug(
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                "Setting max_num_batched_tokens to %d for %s usage context.",
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                self.max_num_batched_tokens, use_context_value)
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        if self.max_num_seqs is None:
            self.max_num_seqs = default_max_num_seqs

            logger.debug("Setting max_num_seqs to %d for %s usage context.",
                         self.max_num_seqs, use_context_value)
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@dataclass
Zhuohan Li's avatar
Zhuohan Li committed
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class AsyncEngineArgs(EngineArgs):
Woosuk Kwon's avatar
Woosuk Kwon committed
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    """Arguments for asynchronous vLLM engine."""
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    disable_log_requests: bool = False
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    @staticmethod
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    def add_cli_args(parser: FlexibleArgumentParser,
                     async_args_only: bool = False) -> FlexibleArgumentParser:
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        # Initialize plugin to update the parser, for example, The plugin may
        # adding a new kind of quantization method to --quantization argument or
        # a new device to --device argument.
        load_general_plugins()
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        if not async_args_only:
            parser = EngineArgs.add_cli_args(parser)
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        parser.add_argument('--disable-log-requests',
                            action='store_true',
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                            help='Disable logging requests.')
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        from vllm.platforms import current_platform
        current_platform.pre_register_and_update(parser)
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        return parser
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def _raise_or_fallback(feature_name: str, recommend_to_remove: bool):
    if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1:
        raise NotImplementedError(
            f"VLLM_USE_V1=1 is not supported with {feature_name}.")
    msg = f"{feature_name} is not supported by the V1 Engine. "
    msg += "Falling back to V0. "
    if recommend_to_remove:
        msg += f"We recommend to remove {feature_name} from your config "
        msg += "in favor of the V1 Engine."
    logger.warning(msg)


def _warn_or_fallback(feature_name: str) -> bool:
    if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1:
        logger.warning(
            "Detected VLLM_USE_V1=1 with %s. Usage should "
            "be considered experimental. Please report any "
            "issues on Github.", feature_name)
        should_exit = False
    else:
        logger.info(
            "%s is experimental on VLLM_USE_V1=1. "
            "Falling back to V0 Engine.", feature_name)
        should_exit = True
    return should_exit


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def human_readable_int(value):
    """Parse human-readable integers like '1k', '2M', etc.
    Including decimal values with decimal multipliers.
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    Examples:
    - '1k' -> 1,000
    - '1K' -> 1,024
    - '25.6k' -> 25,600
    """
    value = value.strip()
    match = re.fullmatch(r'(\d+(?:\.\d+)?)([kKmMgGtT])', value)
    if match:
        decimal_multiplier = {
            'k': 10**3,
            'm': 10**6,
            'g': 10**9,
        }
        binary_multiplier = {
            'K': 2**10,
            'M': 2**20,
            'G': 2**30,
        }

        number, suffix = match.groups()
        if suffix in decimal_multiplier:
            mult = decimal_multiplier[suffix]
            return int(float(number) * mult)
        elif suffix in binary_multiplier:
            mult = binary_multiplier[suffix]
            # Do not allow decimals with binary multipliers
            try:
                return int(number) * mult
            except ValueError as e:
                raise argparse.ArgumentTypeError("Decimals are not allowed " \
                f"with binary suffixes like {suffix}. Did you mean to use " \
                f"{number}{suffix.lower()} instead?") from e

    # Regular plain number.
    return int(value)


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# These functions are used by sphinx to build the documentation
def _engine_args_parser():
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    return EngineArgs.add_cli_args(FlexibleArgumentParser())
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def _async_engine_args_parser():
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    return AsyncEngineArgs.add_cli_args(FlexibleArgumentParser(),
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                                        async_args_only=True)